As the volume of wireless communications increases, newer techniques and combinations of techniques have been developed to increase efficiency and capacity for available communications spectrum. Recently, the multiple-input multiple output (MIMO) technique has been developed and deployed in a range of wireless technologies to address the need for greater data transmission rates. The MIMO technique entails the use of multiple antennae at the transmitter and receiver, where the number of receiver antennae equals or exceeds the number of transmitting antennae. By virtue of the use of multiple antennae, multiple data streams can be transmitted simultaneously from sender to receiver over the same frequency range, thereby increasing the channel transmission capacity in proportion to the number of transmitting antennae.
One MIMO scheme involves so-called Spatial Multiplexing (SM). In a SM scheme, independent streams of data are transmitted from each antenna using the same time and frequency resource. One problem that results from the fading channels exploited by MIMO systems is an increase in intersymbol interference (ISI). The orthogonal frequency division multiplexing (OFDM) technique suppresses the ISI and has therefore been adopted for deploying in combination with MIMO transmissions in various present day radio technologies.
In an OFDM/MIMO system, for instance, independent symbols are transmitted by a multiple antennae transmitting device from each antenna in the same OFDM symbol and the same subcarrier location. The streams of data combine in the air, generating interference with one another. The symbols may then be received on multiple receive antennas and are then processed in a MIMO decoder that separates the independent streams of data.
In particular, channel decoders (e.g., turbo or Low-Density Parity Check (LDPC) decoders) may be employed to properly decode the received coded waveforms. A typical input used by such decoders to perform decoding is the Logarithm of the Likelihood Ratio (LLR). The optimal output of a MIMO detector may then constitute the log-likelihood ratio (LLR) of each bit transmitted in a vector. For a single received bit, a LLR is the natural logarithm of the ratio of the likelihood functions that the originally transmitted bit was either “1” or “0”.
In present day technology receivers are typically set to implement the LLR calculation using the well known maximum likelihood (ML) algorithm, which yields relatively higher performance, at the expense of increased power consumption. In particular, conventional ML decoding provides improved detection by virtue of the better ability to successfully determine which symbols are transmitted in different streams of a MIMO communication. However, the conventional ML approach suffers from complexity that increases exponentially with the number of antennae employed in the MIMO system and the modulation order.
An alternative approach that may be employed to generate LLRs is the use of the well known max-log approximation algorithm, which requires fewer calculations and results in lower power consumption by a receiver, although performance may be sacrificed. In present day wireless devices that employ only max-log calculations for LLR generation device power and cost may be reduced. However, device performance may be degraded over a wide range of MIMO channels.
Depending on the scenario for reception of a given communications signal, such as a set of symbols received by a MIMO receiver, it may be preferable to employ ML LLR generation as opposed to max-log LLR generation. However, present day wireless devices are generally configured to perform exclusively either ML or max-log decoding.
It is with respect to these and other considerations that the present improvements have been needed.